Subject omission/production in child bilingual English and child bilingual Spanish: the view from linguistic theory
Bibliographic record
Abstract
Abstract In bilingual child language acquisition research, a recurrent learnability issue has been to investigate whether and how cross-linguistic influence would interact with the non-adult patterns of omission/production of functional categories. In this paper, we analyze the omission/production of subject pronouns in the earliest stage English grammar and the earliest stage Spanish grammar of two English–Spanish simultaneous bilingual children (FerFuLice corpus in CHILDES). We base this analysis on Holmberg’s (2005, Is there a little pro? Evidence from Finnish. Linguistic Inquiry 36. 533–564) and Sheehan’s (2006, The EPP and null subjects in Romance. Newcastle: Newcastle University PhD dissertation) formulation of the null subject parameter and on Liceras et al.’s (2012, Overt subjects and copula omission in the Spanish and the English grammar of English-Spanish bilinguals: On the locus and directionality of interlinguistic influence. First Language 32(1–2). 88–115) assumptions concerning the role of lexical specialization in cross-linguistic influence. We have conducted a comparative analysis of the patterns of production/omission of English and Spanish overt and null subjects in two bilingual children, on the one hand, versus the patterns of production/omission of one monolingual English child and one monolingual Spanish child, on the other. The results show that while there is no conclusive evidence as to whether or not English influences the higher production of overt subjects in child bilingual Spanish, the presence of null subjects in Spanish has a positive influence in the eradication of non-adult null subjects in bilingual English. We argue that in a bilingual situation, as compared to a monolingual one, lexical specialization in one of the languages of the bilinguals (the availability of an overt and a null realization of the subject in Spanish) facilitates the acquisition of the other language.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".